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Efficient Aging-Aware SRAM Failure Probability Calculation via Particle Filter-Based Importance Sampling
Hiromitsu AWANO Masayuki HIROMOTO Takashi SATO
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences
Publication Date: 2016/07/01
Online ISSN: 1745-1337
Type of Manuscript: Special Section PAPER (Special Section on Design Methodologies for System on a Chip)
SRAM cell yield, failure probability calculation, NBTI, importance sampling, particle filter, Monte Carlo method,
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An efficient Monte Carlo (MC) method for the calculation of failure probability degradation of an SRAM cell due to negative bias temperature instability (NBTI) is proposed. In the proposed method, a particle filter is utilized to incrementally track temporal performance changes in an SRAM cell. The number of simulations required to obtain stable particle distribution is greatly reduced, by reusing the final distribution of the particles in the last time step as the initial distribution. Combining with the use of a binary classifier, with which an MC sample is quickly judged whether it causes a malfunction of the cell or not, the total number of simulations to capture the temporal change of failure probability is significantly reduced. The proposed method achieves 13.4× speed-up over the state-of-the-art method.